can influence the dependability of an output or product derived from spatial analysis Devillers et al, 2010b. Consequently,
research has sought to inform methods for gauging errors in vector data Harding, 2010 as well as modelling Stein and
Van Oort, 2010 and managing uncertainty Fisher et al, 2010. In addition, some studies have sought to investigate quality of
raster datasets such as remote sensing imagery, by analysing aspects of the workflow from the data collection to the
processing and analysis of the images Riazanoff and Santer, 2010. These aspects include for instance, radiometric, spectral,
atmospheric and geometric corrections. It is incumbent on data providers to assess the quality of the spatial products Harding,
2010
The aim of this paper is therefore to conduct a quality analysis of a participatory GIS developed for a case study site in Cape
Town. In order to do so, the paper will first present the current ‘state-of-the-art’ in spatial data quality assessment. The
prescribed quality assessment methods will then be applied in the context of PGIS. The next section highlights prescribed
methods for assessing spatial quality.
2. SPATIAL DATA QUALITY
2.1 Concepts of Data Quality
A spatial dataset is basically a simplified version or representation of a real spatial environment and therefore spatial
quality is a measure of the difference between the model and the reality it represents Docan, 2013. Thus, Chrisman 2010
noted that discussion on spatial quality had evolved from analysis of positional error to include factors such as attribute
accuracy, topology and fitness for purpose. Devillers and Jeansoulin 2010a further distinguished two main components
of spatial quality namely internal and external quality. Internal quality corresponds to the extent of similarity between the data
produced and the ideal data that should have been produced. External quality corresponds to the level of conformance that
exists between a spatial data product and the end user
s’ needs in a given context Devillers and Jeansoulin, 2010. The concept
of external quality implies that the same dataset can be perceived to have different quality to different users and thus
external quality is not absolute. Most definitions on quality are often associated with external quality. For instance, Docan
2013 describes quality as ‘the totality of characteristics of a product that bear on its ability to satisfy stated and implied
needs ’.
2.2 Data Quality Parameters
Geospatial observations describe phenomena with three key components i.e. spatial, temporal and thematic components.
Veregin 1999 posits that space, which is primarily concerned with geographical location, has long been the most highlighted
component when assessing quality of geospatial observations. Further, Veregin 1999 highlights the fact that time should be
an important component of spatial quality assessment especially because events manifest in both space and time. Additionally,
the study argues that whilst it is true that without space there is nothing geographical about the data, the theme is also very
relevant, because without it, there is only geometry. In other
words, describing the ‘what’ is just as relevant as describing the ‘where’.
2.3 Data Quality Components
